Abstract
Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-to-day data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.